Downloading GIS Data

Introduction:

The goal of this lab was to learn how to access, download, and map data from the U.S. Census Bureau. I was responsible for understanding the U.S. Census data and then picking a variable of my choice to download and map on ArcMap.

Methods:

I began this lab by familiarizing myself with the 2010 results published by the US Census Bureau. I also explored the different topics, geographies, and datasets that one could use within the websites search menu.

Objective One - I had to download the total population for all counties within the state of Wisconsin. Once I downloaded and unzipped that information then I had to save the csv files as an Excel Workbook. After that task was accomplished the files that I downloaded only contained tabular data, which means that the information is not associated to the geography or spatial representation for the Wisconsin counties.

Objective Two - At this step I ran into some terrible maneuvering through the US Census information. I had to switch the year in the search menu because the map tab under geography would not recognize the 2014 dataset. When I went to download the information the website was having trouble processing my request. After getting through that issue I finally was able to download the data. Then Internet Explore did not recognize the websites information so I had to change the computers settings so it would recognize the files that were being processed on the US Census Bureau’s website. Finally, I was able to download the files without any kinks.

Objective Three – I started a new blank map in ArcMap and uploaded the 050_00 shapefile and the P1 table onto the map. Next I examined the attribute tables for both the shapefile and the P1 table. In order to join the two tables I had to determine which attribute field the two tables had in common. I found the both the 050_00 shapefile and the P1 table had the GEO_ID field in common. You cannot join two tables together without determining a common attribute field to base the join off of. After that I conducted a table join between the 050_00 shapefile and the P1 table. I this point I had successfully uploaded an MS Excel file directly into ArcMap and performed a table join.

Objective Four – Once I completed joining the tables I was able to proceed and map the specific information that I was interested in. When I went into the symbology tab to map the total population for Wisconsin counties I ran into an error. The field type that I was interested in mapping could not be mapped quantitatively. In order to correct this problem I had to go ahead and add a field to the 050_00 shapefile attribute table. I renamed the new field as D001new. Then I had to use the field calculator tool to populate my newly created field. Once the D001new field was calculated then I could move forward and map Wisconsin’s total population by county quantitatively.   

Objective Five – For this objective I was responsible for selecting a variable of my choice from the U.S. Census Bureau and mapping it. Originally I wanted to map the characteristics of veterans who either have served or are currently serving in the United States Armed Forces. However, when I looked at the dataset none of the statistics were produced in the 2010 SF1 100% data, which forced me to pick another variable. I decided to download data by Sex and Age from the Census website. Specifically I chose the 21-year-old sample for males because my brother just turned 21, and I was curious what the male 21-year-old demographic looked like across Wisconsin. Throughout this objective I followed the same steps that I did in objectives one through four. I will admit that after already producing a map in this fashion it was easy to replicate another map.    

Results:

The results from the total population map shows that mostly counties that are highly urbanized have a higher concentration of inhabitants. For example, Dane County and Milwaukee County appear to have the highest population concentration when compared to the rest of Wisconsin. As for the map depicting 21-year-old males in Wisconsin, the results are similar to the total population map but a few differences are apparent. For example, Douglas County, Eau Claire County, and La Crosse County now are comparable to that of Dane County and Milwaukee County. This makes sense because these five counties are home to some of the biggest private and public educational institutions throughout Wisconsin. Which makes these five counties highly populated with 21-year-old male students.       

Figure 1:

Figure 1 Shows population statistics by counties in Wisconsin. The map on the left depicts Wisconsin’s total population by county. The map on the right depicts the male population that falls within the 21-year-old age bracket. 

Source: U.S. Census Bureau Website

http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t